trying other gen ai

gen.py

@@ -1,20 +1,20 @@
-# from transformers import AutoModelForCausalLM, AutoTokenizer
-# import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
 
-# tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-# model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
-# # Let's chat for 5 lines
-# for step in range(5):
-#     # encode the new user input, add the eos_token and return a tensor in Pytorch
-#     new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
+# Let's chat for 5 lines
+for step in range(50):
+    # encode the new user input, add the eos_token and return a tensor in Pytorch
+    new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
 
-#     # append the new user input tokens to the chat history
-#     bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
+    # append the new user input tokens to the chat history
+    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
 
-#     # generated a response while limiting the total chat history to 1000 tokens, 
-#     chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+    # generated a response while limiting the total chat history to 1000 tokens, 
+    chat_history_ids = model.generate(bot_input_ids, max_length=10000, pad_token_id=tokenizer.eos_token_id)
 
-#     # pretty print last ouput tokens from bot
-#     print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
+    # pretty print last ouput tokens from bot
+    print("Deca: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))

triage.py

@@ -0,0 +1,24 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+
+
+def gen():
+    step =0
+    while True:
+        # encode the new user input, add the eos_token and return a tensor in Pytorch
+        new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
+
+        # append the new user input tokens to the chat history
+        bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
+
+        # generated a response while limiting the total chat history to 1000 tokens, 
+        chat_history_ids = model.generate(bot_input_ids, max_length=10000, pad_token_id=tokenizer.eos_token_id)
+
+        # pretty print last ouput tokens from bot
+        print("Deca: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
+
+gen()